{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a tutorial for performing integration between scATAC Metacells and scRNA Metacells when multiome data is not available. The integration is performed between Metacell gene expression and Metacell gene scores using linear transport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:55:16.409600Z",
     "start_time": "2022-03-31T05:55:14.111908Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:55:16.502929Z",
     "start_time": "2022-03-31T05:55:16.411665Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:55:16.512049Z",
     "start_time": "2022-03-31T05:55:16.507094Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Some plotting aesthetics\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('ticks')\n",
    "matplotlib.rcParams['figure.figsize'] = [4, 4]\n",
    "matplotlib.rcParams['figure.dpi'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:55:17.076373Z",
     "start_time": "2022-03-31T05:55:17.072980Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def log_transform(ad, ps=0.1):\n",
    "    ad.X.data = np.log2(ad.X.data + ps) - np.log2(ps)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n",
    "\n",
    "We recommend the use of scanpy Anndata objects as the preferred mode of loading and filtering data.\n",
    "\n",
    "A sample datset is available for download with the instructions listed below. \n",
    "\n",
    "Uncomment the following lines to download the sample dataset in a Unix-based system. For non-UNIX systems, download the files using the URL\n",
    "\n",
    "The datasets below are Anndata objects for the CD34 multiome data generated for use in the SEACells manuscript\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:55:17.476898Z",
     "start_time": "2022-03-31T05:55:17.474627Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# !mkdir data/\n",
    "# !wget https://dp-lab-data-public.s3.amazonaws.com/SEACells/cd34_multiome_rna_with_labels.h5ad -O data/cd34_multiome_rna_with_labels.h5ad\n",
    "# !wget https://dp-lab-data-public.s3.amazonaws.com/SEACells/cd34_multiome_atac_with_labels.h5ad -O data/cd34_multiome_atac_with_labels.h5ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:56:43.275003Z",
     "start_time": "2022-03-31T05:56:42.257939Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "940d209143a5464095e4533a5531bb6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/64 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/seacells/lib/python3.8/site-packages/SEACells/core.py:558: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  meta_ad = sc.AnnData(csr_matrix(summ_matrix))\n"
     ]
    }
   ],
   "source": [
    "# Load the data using scanpy\n",
    "\n",
    "# This object contains raw counts and the results of SEACells\n",
    "rna_ad = sc.read(f'data/cd34_multiome_rna_with_labels.h5ad')\n",
    "\n",
    "from SEACells.core import summarize_by_SEACell\n",
    "rna_meta_ad = summarize_by_SEACell(rna_ad, SEACells_label='SEACell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:56:43.306257Z",
     "start_time": "2022-03-31T05:56:43.277608Z"
    }
   },
   "outputs": [],
   "source": [
    "rna_meta_ad.obs['celltype'] = rna_ad.obs.groupby('SEACell').apply(lambda x: pd.Series(x['celltype']).mode())#%%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:57:02.107671Z",
     "start_time": "2022-03-31T05:57:01.072566Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Normalize and log transform the data\n",
    "sc.pp.normalize_per_cell(rna_meta_ad, counts_per_cell_after=10000)\n",
    "log_transform(rna_meta_ad)\n",
    "\n",
    "# Select highly variable genes\n",
    "sc.pp.highly_variable_genes(rna_meta_ad)\n",
    "\n",
    "# Perform PCA and UMAPs\n",
    "sc.tl.pca(rna_meta_ad, n_comps=50, use_highly_variable=True)\n",
    "sc.pp.neighbors(rna_meta_ad, n_neighbors=5)\n",
    "sc.tl.umap(rna_meta_ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:57:02.229647Z",
     "start_time": "2022-03-31T05:57:02.109852Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 570.24x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Plot cell-types for reference\n",
    "sc.pl.scatter(rna_meta_ad, basis='umap', color='celltype', frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# ATAC-seq gene scores\n",
    "\n",
    "We use the gene scores derived from ArchR aggregated by SEACell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:57:19.102764Z",
     "start_time": "2022-03-31T05:57:17.535597Z"
    }
   },
   "outputs": [],
   "source": [
    "atac_ad = sc.read(f'data/cd34_multiome_atac_with_labels.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:57:22.143447Z",
     "start_time": "2022-03-31T05:57:19.922453Z"
    }
   },
   "outputs": [],
   "source": [
    "# Aggregate gene scores\n",
    "genescores = pd.DataFrame(atac_ad.obsm['GeneScores'])\n",
    "genescores.columns = rna_meta_ad.var_names\n",
    "genescores.index = atac_ad.obs_names\n",
    "\n",
    "genescores = genescores.join(atac_ad.obs['SEACell']).groupby('SEACell').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:57:39.842039Z",
     "start_time": "2022-03-31T05:57:39.831333Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create Anndata\n",
    "import anndata\n",
    "atac_meta_ad = anndata.AnnData(genescores)\n",
    "atac_meta_ad.obs = atac_ad.obs.loc[atac_meta_ad.obs_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:57:49.512155Z",
     "start_time": "2022-03-31T05:57:49.457242Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "atac_meta_ad.X = csr_matrix(atac_meta_ad.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T05:59:47.081290Z",
     "start_time": "2022-03-31T05:59:47.006612Z"
    }
   },
   "outputs": [],
   "source": [
    "# Normalize the ATAC counts analogous to RNA counts\n",
    "sc.pp.normalize_per_cell(atac_meta_ad, counts_per_cell_after=10000)\n",
    "log_transform(atac_meta_ad)\n",
    "\n",
    "# Compute PCA using the highly variable genes from RNA \n",
    "hvg = atac_meta_ad.var_names.intersection(rna_meta_ad.var_names[rna_meta_ad.var['highly_variable']])\n",
    "atac_hvg = pd.Series(False, index=atac_meta_ad.var_names)\n",
    "atac_hvg[hvg] = True\n",
    "atac_meta_ad.var['highly_variable'] = atac_hvg\n",
    "sc.tl.pca(atac_meta_ad, n_comps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:00:00.999779Z",
     "start_time": "2022-03-31T06:00:00.192913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 570.24x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ATAC gene scores umap\n",
    "sc.pp.neighbors(atac_meta_ad, use_rep='X_pca', n_neighbors=5)\n",
    "sc.tl.umap(atac_meta_ad)\n",
    "sc.pl.scatter(atac_meta_ad, basis='umap', color='celltype', frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:05:00.191466Z",
     "start_time": "2022-03-31T06:05:00.152673Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/seacells/lib/python3.8/site-packages/anndata/_core/anndata.py:1785: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n",
      "  [AnnData(sparse.csr_matrix(a.shape), obs=a.obs) for a in all_adatas],\n"
     ]
    }
   ],
   "source": [
    "# Annotate the Anndata objects based on their dataset\n",
    "atac_meta_ad.obs['Dataset'] = 'ATAC'\n",
    "rna_meta_ad.obs['Dataset'] = 'RNA'\n",
    "\n",
    "# Create a combined Anndata object only including hvg\n",
    "hvg = atac_meta_ad.var_names[atac_meta_ad.var['highly_variable']].intersection(rna_meta_ad.var_names)\n",
    "comb_ad = rna_meta_ad[:, hvg].concatenate(atac_meta_ad[:, hvg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:05:14.756641Z",
     "start_time": "2022-03-31T06:05:14.724360Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# PCA \n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "svd = TruncatedSVD(n_components=30)\n",
    "comb_ad.obsm['X_pca'] = svd.fit_transform(comb_ad.X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integration using linear transport\n",
    "\n",
    "Inputs are\n",
    "\n",
    "- xs, Matrix of \"source\" RNA expression levels from scRNA-seq\n",
    "- xt, Matrix of \"target\" gene activities calcualated from ATAC-seq peak accessibility\n",
    "\n",
    "\n",
    "The number of metacells can be variable between the two modalities but the number of columns should remain the same. We will use the jointly computed PCs as input for integration\n",
    "\n",
    "Optional paramters\n",
    "\n",
    "- ws: Vector of weights representing the size of each cluster/metacell from RNA\n",
    "- wt: Same as above but for ATAC\n",
    "- rho: Float in range [0,1] representing whether the final transformation should be closer to RNA distribution (0) or ATAC distribution (1). Default value is 1.\n",
    "- reg: Small float to make sure covariance matrices are invertible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:05:15.671219Z",
     "start_time": "2022-03-31T06:05:15.657432Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "xs = comb_ad[comb_ad.obs['Dataset'] == 'RNA'].obsm['X_pca']\n",
    "xt = comb_ad[comb_ad.obs['Dataset'] == 'ATAC'].obsm['X_pca']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:05:16.826766Z",
     "start_time": "2022-03-31T06:05:16.820691Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from SEACells.domainadapt import LinearOT\n",
    "\n",
    "model = LinearOT(rho=0)\n",
    "\n",
    "xs_transformed, xt_transformed = model.fit_transform(xs, xt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:05:17.887782Z",
     "start_time": "2022-03-31T06:05:17.870603Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x3/p87l8d1n5qvcxb8hjggq4cg80000gq/T/ipykernel_84957/2157489645.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  comb_ad.obsm['X_pca_transformed'] = pd.DataFrame(xs_transformed).append(pd.DataFrame(xt_transformed)).values\n"
     ]
    }
   ],
   "source": [
    "comb_ad.obsm['X_pca_transformed'] = pd.DataFrame(xs_transformed).append(pd.DataFrame(xt_transformed)).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integration result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:06:01.550339Z",
     "start_time": "2022-03-31T06:06:01.540069Z"
    }
   },
   "outputs": [],
   "source": [
    "sc.pp.neighbors(comb_ad, use_rep='X_pca_transformed', n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:06:02.688436Z",
     "start_time": "2022-03-31T06:06:02.041533Z"
    }
   },
   "outputs": [],
   "source": [
    "sc.tl.umap(comb_ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:06:21.972362Z",
     "start_time": "2022-03-31T06:06:21.747901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1140.48x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.scatter(comb_ad, basis='umap', color=['celltype', 'Dataset'], frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping ATAC SEACells to RNA SEACellss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:06:29.524259Z",
     "start_time": "2022-03-31T06:06:29.520866Z"
    }
   },
   "outputs": [],
   "source": [
    "# Mapping ATAC SEACells to RNA SEACellss\n",
    "rna_PCs = pd.DataFrame(xs_transformed)\n",
    "atac_PCs = pd.DataFrame(xt_transformed)\n",
    "\n",
    "rna_PCs.index = rna_meta_ad.obs_names\n",
    "atac_PCs.index = atac_meta_ad.obs_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:06:29.813784Z",
     "start_time": "2022-03-31T06:06:29.806409Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_distances(df1, df2):\n",
    "    result = pd.DataFrame(np.zeros((df1.shape[0],df2.shape[0]))).set_index(df1.index)\n",
    "    result.columns = df2.index\n",
    "\n",
    "    for mc1, row1 in df1.iterrows():\n",
    "        for mc2, row2 in df2.iterrows():\n",
    "            result.loc[mc1, mc2] = np.sqrt(((row1-row2)**2).sum())\n",
    "\n",
    "    return result\n",
    "\n",
    "def get_match_ranks(t,k=5):\n",
    "    \"\"\"\n",
    "    Compute mutually ranked metacell matches.\n",
    "    \"\"\"\n",
    "    arr = t.values.T\n",
    "    ind = np.argsort(arr, axis=0)[:k].T\n",
    "\n",
    "    df = pd.DataFrame(t.columns.values[ind])\n",
    "    df.index = t.index\n",
    "    \n",
    "    return df\n",
    "\n",
    "def MNN(rna_PCs, atac_PCs, k=3):\n",
    "    \"\"\"\n",
    "    Construct a mapping between RNA and ATAC SEACells, if an RNA SEACell and \n",
    "    ATAC SEACell are mutually in each others' k-nearest neighbors.\n",
    "    \n",
    "    Nearest neighbors are computed based on distance in transformed PC space,\n",
    "    following domain adaption by optimal transport.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Compute distances between PCs in each data set\n",
    "    dists = compute_distances(rna_PCs, atac_PCs)\n",
    "    \n",
    "    # Find the top k nearest neighbors in each row of the distance matrix\n",
    "    r1 = get_match_ranks(dists, k)\n",
    "    r2 = get_match_ranks(dists.T, k)\n",
    "    # Add edges between samples if they appear in each others top-ranks \n",
    "\n",
    "    edges = []\n",
    "    for index, row in r1.iterrows():\n",
    "        for nbr in row.values:\n",
    "            if index in r2.loc[nbr].values:\n",
    "                edges.append((index, nbr))\n",
    "\n",
    "    edges = pd.DataFrame(edges)\n",
    "    edges.columns = ['RNA', 'ATAC']\n",
    "    return edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-31T06:06:32.033750Z",
     "start_time": "2022-03-31T06:06:30.640781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RNA</th>\n",
       "      <th>ATAC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cd34_multiome_rep2#CATTCCTCACCCACAG-1</td>\n",
       "      <td>cd34_multiome_rep1#GGCGGTAAGTAGCGGG-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cd34_multiome_rep2#CATTCCTCACCCACAG-1</td>\n",
       "      <td>cd34_multiome_rep1#TACAGCTAGACCATAC-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cd34_multiome_rep2#CAATCGCCACGTAAGG-1</td>\n",
       "      <td>cd34_multiome_rep2#TAACCTAAGGATTGAG-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cd34_multiome_rep1#CCAAGTTAGGACCTCA-1</td>\n",
       "      <td>cd34_multiome_rep1#ATGGCTGTCAATGTCA-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cd34_multiome_rep1#CCAAGTTAGGACCTCA-1</td>\n",
       "      <td>cd34_multiome_rep1#ATTGCACAGTCAATTG-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     RNA  \\\n",
       "0  cd34_multiome_rep2#CATTCCTCACCCACAG-1   \n",
       "1  cd34_multiome_rep2#CATTCCTCACCCACAG-1   \n",
       "2  cd34_multiome_rep2#CAATCGCCACGTAAGG-1   \n",
       "3  cd34_multiome_rep1#CCAAGTTAGGACCTCA-1   \n",
       "4  cd34_multiome_rep1#CCAAGTTAGGACCTCA-1   \n",
       "\n",
       "                                    ATAC  \n",
       "0  cd34_multiome_rep1#GGCGGTAAGTAGCGGG-1  \n",
       "1  cd34_multiome_rep1#TACAGCTAGACCATAC-1  \n",
       "2  cd34_multiome_rep2#TAACCTAAGGATTGAG-1  \n",
       "3  cd34_multiome_rep1#ATGGCTGTCAATGTCA-1  \n",
       "4  cd34_multiome_rep1#ATTGCACAGTCAATTG-1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = MNN(rna_PCs, atac_PCs)\n",
    "mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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